This paper presents a novel distributed active set method for model predictive control of linear systems. The combines primal strategy with decentralized conjugate gradient to solve convex quadratic programs. An advantage the proposed compared existing algorithms is feasibility iterates. Numerical results show that can compete alternating direction multipliers in terms communication requirement...

We present an algorithm for the minimization of a nonconvex quadratic function subject to linear inequality constraints and two-sided bound on 2-norm its solution. The minimizes objective using active-set method by solving series Trust-Region Subproblems (TRS). Underpinning efficiency this approach is that global solution TRS has been widely studied in literature, resulting remarkably efficient...

Change is a constant factor in software development process It should come as side effect Domino effect: Change to sequence diagrams impacts the corresponding collaboration diagrams. .. Redesign class models impacts on state machines Pattern Application Structure of State Machines [UML Standard] If a composite state is active and not orthogonal, at most one of its substates is active Each regio...

In this paper, the problem of a retaining wall under active translation mode is investigated numerically. To this end, a series of numerical models is conducted using the discrete element code, PFC2D. The backfill soil is simulated by an assembly of separate cohesionless circular particles. Backfill soil was prepared by pouring soil particles from a specific height under gravity force and givin...

State estimation in the energy management center of active distribution networks has attracted many attentions. Considering an increase in complexity and real-time management of active distribution networks and knowing the network information at each time instant are necessary. This article presents a two-step multi-area state estimation method in balanced active distribution networks. The prop...

In our previous work we have discussed the training method of a support vector classifier by active set training allowing the solution to be infeasible during training. In this paper, we extend this method to training a support vector regressor (SVR). We use the dual form of the SVR where variables take real values and in the objective function the weighted linear sum of absolute values of the ...

Active contours, very popular in image segmentation, suffer from delicate adjustments of many parameters. We propose to carry out these adjustments using genetic algorithm. Here an active contour is implemented using a greedy algorithm. Within this framework, two approaches are presented. A supervised approach which delivers a global set of parameters. In this case the greedy algorithm is invol...

This chapter describes an active-set algorithm for the solution of quadratic programming problems in the context of Support Vector Machines (SVMs). Most of the common SVM optimizers implement working-set algorithms like the SMO method because of their ability to handle large data sets. Although they show generally good results, they may perform weakly in some situations, e.g., if the problem is...